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Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature
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Institution: |
1Inserm U930, Tours, France; Université François Rabelais de Tours, Faculté de Médecine, IFR 135 < Imagerie fonctionnelle >, Tours, France; CHRU de Tours, Tours, France; CNRS ERL 3106, Tours, France. 2Athinoula A. Martinos Center for Biomedical Imaging, NMR Center, Harvard Medical School, Charlestown, MA, USA 3University of California San Diego, Departments of Radiology and Neurosciences, La Jolla, CA, USA. |
Publisher: |
Elsevier Science |
Publication Date: |
Oct-2010 |
Journal: |
Neuroimage |
Volume Number: |
53 |
Issue Number: |
1 |
Pages: |
1-15 |
Citation: |
Neuroimage. 2010 Oct 15;53(1):1-15. |
PubMed ID: |
20547229 |
PMCID: |
PMC2937159 |
Keywords: |
Anatomy, Atlas, Brain, Cerebral cortex, MRI |
Appears in Collections: |
NA-MIC |
Sponsors: |
U54 EB005149-01 P41 RR14075/RR/NCRR NIH HHS/United States R01 EB001550/EB/NIBIB NIH HHS/United States R01 EB009282/EB/NIBIB NIH HHS/United States R01 EB006758/EB/NIBIB NIH HHS/United States U24 RR021382/RR/NCRR NIH HHS/United States |
Generated Citation: |
Destrieux C., Fischl B., Dale A., Halgren E. Automatic Parcellation of Human Cortical Gyri and Sulci using Standard Anatomical Nomenclature. Neuroimage. 2010 Oct 15;53(1):1-15. PMID: 20547229. PMCID: PMC2937159. |
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Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology.
Additional Material
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